Pharmacological Management of Chronic Neuropathic Pain – Consensus Statement and Guidelines from the Canadian Pain Society
Bibliographic record
Abstract
Neuropathic pain (NeP), generated by disorders of the peripheral and central nervous system, can be particularly severe and disabling. Prevalence estimates indicate that 2% to 3% of the population in the developed world suffer from NeP, which suggests that up to one million Canadians have this disabling condition. Evidence-based guidelines for the pharmacological management of NeP are therefore urgently needed. Randomized, controlled trials, systematic reviews and existing guidelines focusing on the pharmacological management of NeP were evaluated at a consensus meeting. Medications are recommended in the guidelines if their analgesic efficacy was supported by at least one methodologically sound, randomized, controlled trial showing significant benefit relative to placebo or another relevant control group. Recommendations for treatment are based on degree of evidence of analgesic efficacy, safety, ease of use and cost-effectiveness. Analgesic agents recommended for first-line treatments are certain antidepressants (tricyclics) and anticonvulsants (gabapentin and pregabalin). Second-line treatments recommended are serotonin noradrenaline reuptake inhibitors and topical lidocaine. Tramadol and controlled-release opioid analgesics are recommended as third-line treatments for moderate to severe pain. Recommended fourth-line treatments include cannabinoids, methadone and anticonvulsants with lesser evidence of efficacy, such as lamotrigine, topiramate and valproic acid. Treatment must be individualized for each patient based on efficacy, side-effect profile and drug accessibility, including cost. Further studies are required to examine head-to-head comparisons among analgesics, combinations of analgesics, long-term outcomes, and treatment of pediatric and central NeP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".